For evaluating the model introduced in Section  we used four datasets, where two were generated with a known ground truth and two are real world examples. To keep the setup as lucid as possible and to avoid anisotropic issues, we here only consider datasets with 1-dimensional labels \(y\) . For all examples below, we used the \(q_\phi \)  induced by Bernoulli dropout [1]} with a dropout rate of 0.5, an Adam optimizer [2]} and a learning rate of \(10^{-3}\)  that was reduced to \(10^{-4}\)  for the last 50 epochs. Training both, EiV and non-EiV, alongside each other with the same \(q_\phi \)  allows us to judge the quality of our EiV model and serves as a consistency check as we expect the EiV model to behave like the non-EiV model for small Deming factors, \(\delta \searrow 0\) . Due to the multiple sampling of \(\zeta \)  in (REF ) training an EiV model requires more time for the same number of epochs. We used \(L=5\)  and observed an increase of the training time by a factor of about 2. Using a single Tesla K80 GPU a training run of both models took a few minutes for the examples below, with exception of the multinomial example where the training time was around 1/2 h (non-EiV) and 1 h (EiV). All experiments were implemented in the Python library PyTorch [3]} (BSD license).
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